Instructions to use ValiantLabs/Qwen3.5-27B-Esper3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ValiantLabs/Qwen3.5-27B-Esper3.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ValiantLabs/Qwen3.5-27B-Esper3.1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ValiantLabs/Qwen3.5-27B-Esper3.1")
model = AutoModelForCausalLM.from_pretrained("ValiantLabs/Qwen3.5-27B-Esper3.1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ValiantLabs/Qwen3.5-27B-Esper3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ValiantLabs/Qwen3.5-27B-Esper3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Qwen3.5-27B-Esper3.1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ValiantLabs/Qwen3.5-27B-Esper3.1

SGLang

How to use ValiantLabs/Qwen3.5-27B-Esper3.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ValiantLabs/Qwen3.5-27B-Esper3.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Qwen3.5-27B-Esper3.1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ValiantLabs/Qwen3.5-27B-Esper3.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Qwen3.5-27B-Esper3.1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ValiantLabs/Qwen3.5-27B-Esper3.1 with Docker Model Runner:
```
docker model run hf.co/ValiantLabs/Qwen3.5-27B-Esper3.1
```

Qwen3.5-27B-Esper3.1

File size: 4,718 Bytes

---
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- esper
- esper-3.1
- esper-3
- valiant
- valiant-labs
- qwen
- qwen-3.5
- qwen-3.5-27b
- 27b
- reasoning
- code
- code-instruct
- python
- javascript
- dev-ops
- jenkins
- terraform
- ansible
- docker
- jenkins
- kubernetes
- helm
- grafana
- prometheus
- shell
- bash
- azure
- aws
- gcp
- cloud
- scripting
- powershell
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
base_model: Qwen/Qwen3.5-27B
datasets:
- sequelbox/Titanium3-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part2-DeepSeek-V3.2
- sequelbox/Mitakihara-DeepSeek-R1-0528
license: apache-2.0
---


**[Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/qdicXwrO_XOKRTjOu2yBF.jpeg)

Esper 3.1: [Ministral-3-3B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-3B-Reasoning-2512-Esper3.1), [Qwen3-4B-Thinking-2507](https://huggingface.co/ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1), [Ministral-3-8B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-8B-Reasoning-2512-Esper3.1), [Ministral-3-14B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1), [gpt-oss-20b](https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1), [Qwen3.5-27B](https://huggingface.co/ValiantLabs/Qwen3.5-27B-Esper3.1), [Qwen3.6-27B](https://huggingface.co/ValiantLabs/Qwen3.6-27B-Esper3.1), [Qwen3.6-35B-A3B](https://huggingface.co/ValiantLabs/Qwen3.6-35B-A3B-Esper3.1)


Esper 3.1 is a coding, architecture, and DevOps reasoning specialist built on Qwen 3.5 27B.
- Your dedicated DevOps expert: Esper 3.1 maximizes DevOps and architecture helpfulness, powered by [high-difficulty DevOps and architecture data](https://huggingface.co/datasets/sequelbox/Titanium3-DeepSeek-V3.1-Terminus) generated with DeepSeek-V3.1-Terminus!
- Improved coding performance: challenging code-reasoning datasets stretch [DeepSeek-V3.1-Terminus](https://huggingface.co/datasets/sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus) and [DeepSeek-V3.2](https://huggingface.co/datasets/sequelbox/Tachibana3-Part2-DeepSeek-V3.2) to the limits, allowing Esper 3.1 to tackle harder coding tasks!
- AI to build AI: our [high-difficulty AI expertise data](https://huggingface.co/datasets/sequelbox/Mitakihara-DeepSeek-R1-0528) boosts Esper 3.1's MLOps, AI architecture, AI research, and general reasoning skills.
- Small model sizes allow running on local desktop and mobile, plus super-fast server inference!


## Prompting Guide
Esper 3.1 uses the [Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B) prompt format.

Example inference script to get started:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ValiantLabs/Qwen3.5-27B-Esper3.1"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Write a Terraform configuration that uses the `aws_ami` data source to find the latest Amazon Linux 2 AMI. Then, provision an EC2 instance using this dynamically determined AMI ID."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 248069 (</think>)
    index = len(output_ids) - output_ids[::-1].index(248069)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)
```


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)


Esper 3.1 is created by [Valiant Labs.](http://valiantlabs.ca/)

[Check out our HuggingFace page to see all of our models!](https://huggingface.co/ValiantLabs)

We care about open source. For everyone to use.